A joint fit of the mass and redshift distributions of the population of Binary Black Holes detected with Gravitational-Wave observations can be used to obtain constraints on the Hubble parameter and on deviations from General Relativity in the propagation of Gravitational Waves. We first present applications of this technique to the latest catalog of Gravitational-Wave events, focusing on the comparison of different parametrizations for the source-frame mass distribution of Black Hole Binaries. We find that models with more than one feature are favourite by the data, as suggested by population studies, even when varying the cosmology. Then, we discuss perspectives for the use of this technique with third generation Gravitational-Wave detectors, exploiting the recently developed Fisher information matrix Python code GWFAST.
Mancarella, M., Borghi, N., Foffa, S., Genoud-Prachex, E., Iacovelli, F., Maggiore, M., et al. (2022). Gravitational-wave cosmology with dark sirens: state of the art and perspectives for 3G detectors. In Proceedings of the 41st International Conference on High energy physics - ICHEP202. Sissa Medialab Srl [10.22323/1.414.0127].
Gravitational-wave cosmology with dark sirens: state of the art and perspectives for 3G detectors
Mancarella, Michele;
2022
Abstract
A joint fit of the mass and redshift distributions of the population of Binary Black Holes detected with Gravitational-Wave observations can be used to obtain constraints on the Hubble parameter and on deviations from General Relativity in the propagation of Gravitational Waves. We first present applications of this technique to the latest catalog of Gravitational-Wave events, focusing on the comparison of different parametrizations for the source-frame mass distribution of Black Hole Binaries. We find that models with more than one feature are favourite by the data, as suggested by population studies, even when varying the cosmology. Then, we discuss perspectives for the use of this technique with third generation Gravitational-Wave detectors, exploiting the recently developed Fisher information matrix Python code GWFAST.
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